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Contents

• About FoKL
• Installation and Setup
• Use Cases
• User Documentation
• Benchmarks and Papers
• Future Development
• Contact Us
• License
• Citations

About FoKL

FoKL-GPy, or FoKL, is a Python package intended for use in machine learning. The name comes from a unique implementation of Forward variable selection using Karhunen-Loève decomposed Gaussian Processes (GP's) in Python (i.e., FoKL-GPy).

The primary advantages of FoKL are:

• Fast inference on static and dynamic datasets using scalable GP regression
• Significant accuracy retained despite being fast

Some other advantages of FoKL include:

• Export modeled non-linear dynamics as a symbolic equation (i.e., use a GP model in Pyomo)
• Take first and second derivatives of model with respect to any input variable (e.g., gradient)
• User-friendly (e.g., automatic handling of various dataset formats, automatic creation of training set, etc.)
• Easy adjusting of hyperparameters for sweeping through variations in order to find optimal settings
• Ability to save, share, and load models
• Ability to import and evaluate a model without known data (i.e., without training)

To read more about FoKL, please see the Benchmarks and Papers section.

Installation and Setup

From your command-line terminal, FoKL is available through PyPI:

pip install FoKL

Alternatively, the GitHub repository may be cloned to create a local copy in which the examples and documentation will be included:

git clone https://github.com/ESMS-Group-Public/FoKL-GPy

Once installed, import the FoKL module in Python with:

from FoKL import FoKLRoutines

From here, the FoKL class object may be created and its methods accessed. Please see Use Cases to learn more about working with FoKL models.

Use Cases

Please first refer to the following for tutorials and examples:

• Automatically formatting and normalizing datasets
• Training and/or evaluating a model
• Saving and/or loading a model
• Validating model via plot and RMSE
• Integrating models of derivatives
• Converting multiple models to Pyomo
• Solving model in Pyomo with non-linear optimization

Then, see User Documentation as needed.

User Documentation

• FoKLRoutines
  • load
  • FoKL
    • clean
    • generate_trainlog
    • trainset
    • bss_derivatives
    • evaluate_basis
    • evaluate
    • coverage3
    • fit
    • clear
    • to_pyomo
    • save
• fokl_to_pyomo
• getKernels
• GP_integrate

FoKLRoutines

The FoKLRoutines module houses the primary routines for a FoKL model. Namely, these are the load function and FoKL class object.

load

model = FoKLRoutines.load(filename, directory=None)

Load a FoKL class from a file. If failing to load a file and/or directory relative to the run script, ensure the terminal directory is set to that of the run script.

By default, directory is the current working directory that contains the script calling this method. An absolute or relative directory may be defined if the model to load is located elsewhere.

For simplicity, enter the returned output from save as the argument here, i.e., for filename. Do this while leaving directory blank since filename can simply include the directory itself.

FoKL

model = FoKLRoutines.FoKL(**kwargs)

This creates a class object that contains all information relevant to and defining a FoKL model.

Upon initialization, hyperparameters and some other settings are defined with default values as attributes of the FoKL class. These attributes are as follows, and any or all may be specified as a keyword or later updated by redefining the a class attribute.

Type	Keyword Argument	Default Value	Description
hyperparameter	kernel	'Cubic Splines'	Format of basis functions from the BSS-ANOVA kernel to use for training a model ('Cubic Splines' or 'Bernoulli Polynomials')
"	phis	$f($ kernel $)$	Data structure with coefficients for basis functions
"	relats_in	[]	Boolean matrix indicating which input variables and/or interactions should be excluded from the model
"	a	4	Shape parameter of the initial-guess distribution for the observation error variance of the data
"	b	$f($ a, data $)$	Scale parameter of the initial-guess distribution for the observation error variance of the data
"	atau	4	Parameter of the initial-guess distribution for the $\tau^2$ parameter
"	btau	$f($ atau, data $)$	Parameter of the initial-guess distribution for the $\tau^2$ parameter
"	tolerance	3	Influences how long to continue training after additional terms yield diminishing returns
"	burnin	1000	Total number of draws from the posterior for each tested model before the draws draws
"	draws	1000	Total number of draws from the posterior for each tested model after the burnin draws
"	gimmie	False	Boolean to return the most complex model tried instead of the model with the optimum Bayesian information criterion (BIC)
"	way3	False	Boolean to include three-way interactions
"	threshav	0.05	Threshold to propose terms for elimination. Increase to propose and eliminate more terms
"	threshstda	0.5	Threshold to eliminate terms based on standard deviation relative to mean
"	threshstdb	2	Threshold to eliminate terms based on standard deviation independent of mean
"	aic	False	Boolean to use Aikaike information criterion (AIC)
setting	UserWarnings	True	Boolean to print user-warnings (i.e., FoKL warnings) to command terminal
"	ConsoleOutput	True	Boolean to print progress of model training to command terminal

The following methods are embedded within the class object:

Method	Description
clean	Automatically format and normalize user-provided dataset.
generate_trainlog	Generate random indices of the dataset to use as a training set.
trainset	Return the training set.
bss_derivatives	Algebraically calculate partial derivatives of model with respect to input variables.
evaluate_basis	Calculate value of specified basis function at single point along normalized domain.
evaluate	Calculate values of FoKL model for all requested sets of datapoints.
coverage3	Evaluate FoKL model, calculate confidence bounds, calculate RMSE, and produce plot.
fit	Train new FoKL model to best-fit training dataset according to hyperparameters.
clear	Delete attributes from FoKL class so that new models may be trained without new class objects.
to_pyomo	Convert a FoKL model to an expression in a Pyomo model.
save	Save FoKL class with all its attributes to retain model and avoid re-training.

Each method has optional inputs that allow for flexibility in how FoKL is used so that you may leverage these methods for your specific requirements. Please refer to the Use Cases first, then explore the following documentation of each method as needed.

clean

model.clean(inputs, data=None, **kwargs)

Automatically format and normalize datasets. Note that data is not required but should be entered if available; otherwise, leave blank. Multiple options are available to govern the normalization of inputs. See Automatically formatting and normalizing datasets for example usage.

Input	Type	Description	Default
inputs	any	$n \times m$ input matrix $\mathbf{x}$ of $n$ instances by $m$ features in model $\overline{y}=f(\overline{x}_1,...,\overline{x}_m)$	n/a
data	any	$n \times 1$ output vector $\overline{y}$ of $n$ instances in model $\overline{y}=f(\overline{x}_1,...,\overline{x}_m)$	None

Keyword	Type	Description	Default
train	scalar	(0,1] fraction of $n$ instances to use for training	1
AutoTranspose	boolean	assumes $n > m$ and transposes dataset accordingly	True
bit	integer	(16, 32, 64) floating point bits to save dataset as	64
normalize	boolean	to pass formatted dataset to _normalize()	True
minmax	list of [min, max] lists	upper/lower bounds of each input variable	model.minmax
pillow	list of [lower, upper] lists	fraction of span by which to expand [min, max]; or, values on 0-1 scale that [min, max] should map to	0

After calling clean, the now normalized and formatted dataset gets saved as attributes of the FoKL class. Be sure to use these attributes in place of the originally entered inputs and data so that normalization and formatting errors are avoided. The attributes are as follows:

Attribute	Type	Description
model.inputs	$n \times m$ ndarray	normalized and formatted inputs
model.data	$n \times 1$ ndarray	formatted data
model.minmax	list of $m$ lists	[min, max] factors used to normalize inputs to model.inputs
model.trainlog	$n \times 1$ ndarray	logical index of instances from dataset to use as training set

To then access the training set [traininputs, traindata], see trainset.

generate_trainlog

model.trainlog = model.generate_trainlog(train, n=None)

Generate random logical vector of length $n$ with train percent as True. It is expected that generate_trainlog will be called internally by clean and not by the user, though this method is available if sweeping through values of train in order to compare the accuracy of models fitted to training sets of different sizes.

trainset

traininputs, traindata = model.trainset()

Run this line to access the training set, which is simply model.inputs and model.data indexed by model.trainlog. See clean for how model.inputs and model.data get defined and/or generate_trainlog for how model.trainlog gets defined.

bss_derivatives

dy = model.bss_derivatives(**kwargs)

For returning gradient of modeled function with respect to each, or specified, input variable. If user overrides default settings, then 1st and 2nd partial derivatives can be returned for any variables.

Keyword	Type	Description	Default
inputs	-	see model.inputs of clean	model.inputs
kernel	-	see kernel of FoKL	model.kernel
d1	integer (for single) or list of booleans (for multiple)	index of input variable(s) (i.e., state(s)) to use for first partial derivative; see tip below	True
d2	integer (for single) or list of booleans (for multiple)	index of input variable(s) (i.e., state(s)) to use for second partial derivative; see tip below	False
draws	-	see draws of FoKL	model.draws
betas	-	see betas of FoKL	model.betas
phis	-	see phis of FoKL	model.phis
mtx	$(terms-1) \times m$ ndarray	interaction matrix defining terms in FoKL model by indexing basis function order for each term and input variable combination	model.mtx
minmax	-	see minmax of clean	model.minmax
IndividualDraws	boolean	for returning derivative(s) at each draw	False
ReturnFullArray	boolean	for returning $n \times m \times 2$ array with zeros for non-requested states such that indexing is preserved; otherwise, only requested states are squeezed into a 2D matrix where columns correspond to increasing input variable index and derivative order	False

Output	Type	Description	Default
dy	$n \times m \times 2$ ndarray if ReturnFullArray=True, else $n \times m_{\delta}$ where $m_{\delta}$ is the number of partial derivatives requested	derivative of model with respect to input variable(s) (i.e., state(s)) defined by d1 and d2	gradient (i.e., $n \times m_{\delta}$ ndarray where $m_{\delta} =m$ because d1=True, d2=False

Tip:

• To turn off all first-derivatives, set d1=False instead of d1=0. The reason is d1 and d2, if set to an integer, will return the derivative with respect to the input variable indexed by that integer using Python indexing. In other words, for a two-input FoKL model, setting d1=1 and d2=0 will return the first-derivative with respect to the second input (d1=1) and the second-derivative with respect to the first input (d2=0). Alternatively, d1=[False, True] and d2=[True, False] will function the same so that boolean lists may be used in cases where the derivative with respect to more than one state, but not all states, is required.

evaluate_basis

basis = model.evaluate_basis(c, x, kernel=None, d=0)

Evaluate a basis function at a single point by providing coefficients, $x$ value(s), and (optionally) the kernel. This method is primarily used internally by other methods and so is not expected to be used by the user, but is available for testing purposes and to provide insight toward how the basis functions get evaluated.

For evaluating a FoKL model, see evaluate.

Input	Type	Description
c	list of scalars	coefficients of the basis function or its derivative
x	scalar	value of independent variable at which to evaluate the basis function or its derivative

Keyword	Type	Description	Default
kernel	-	see kernel of FoKL	model.kernel
d	integer	order of derivative (where 0 is no derivative)	0

Output	Type	Description
basis	scalar	evaluation of basis function or its derivative at x

If insightful for understanding how to define c, the values of kernel and order d correspond to the following equations at which basis is evaluated:

Kernel	Order	Basis Function $B_i$ or its Derivative
'Cubic Splines'	d=0	$B_i=c_0+c_1 \cdot x+c_2 \cdot x^2+c_3 \cdot x^3 \implies$ c[0] + c[1] * x + c[2] * (x ** 2) + c[3] * (x ** 3)
"	d=1	$\frac{\partial}{\partial x}(B_i)=c_1+2\cdot c_2\cdot x+3\cdot c_3\cdot x^2 \implies$ c[1] + 2 * c[2] * x + 3 * c[3] * (x ** 2)
"	d=2	$\frac{\partial^2}{\partial x^2}(B_i)=2\cdot c_2+6\cdot c_3\cdot x \implies$ 2 * c[2] + 6 * c[3] * x
'Bernoulli Polynomials'	d=0	$B_i=\sum_{k=0}^{i} (c_k \cdot x^k)\implies$ c[0] + sum(c[k] * (x ** k) for k in range(1, len(c)))
"	d=1	$\frac{\partial}{\partial x}(B_i)=\sum_{k=1}^{i} (k \cdot c_k \cdot x^{k-1})\implies$ c[1] + sum(k * c[k] * (x ** (k - 1)) for k in range(2, len(c)))
"	d=2	$\frac{\partial^2}{\partial x^2}(B_i)=\sum_{k=2}^{i} (k \cdot (k-1) \cdot c_k \cdot x^{k-2})\implies$ sum((k - 1) * k * c[k] * (x ** (k - 2)) for k in range(2, len(c)))

When called internally by evaluate, the coefficients c (i.e., $\overline{c}_i$) automatically correspond to $i$ such that $B_i=f(\overline{c}_i)$. For 'Cubic Splines', this is achieved by c = list(model.phis[i - 1][k][phind] for k in range(4)) where phind $=f(x)$. For 'Bernoulli Polynomials', this is achieved by c = model.phis[i - 1].

evaluate

mean = model.evaluate(inputs=None, betas=None, mtx=None, **kwargs)

Evaluate the FoKL model for provided inputs and (optionally) calculate bounds.

Input	Type	Description	Default
inputs	-	see model.inputs of clean	model.inputs
betas	-	see betas of fit	model.betas
mtx	-	see mtx of fit	model.mtx

Keyword	Type	Description	Default
minmax	-	see minmax of clean	None
draws	-	see draws of FoKL	model.draws
clean	boolean	pass inputs to clean if true; note this will override minmax and result in inputs scaled to 0-1	False
ReturnBounds	boolean	return 95% confidence bounds as second output if true	False

If clean=True, then any keywords documented for clean may be used here.

Output	Type	Description
mean	$n \times 1$ ndarray	prediction of $\overline{y}$ in $\overline{y}=f(\overline{x}_1,...,\overline{x}_m)$ for provided inputs; prediction of model.data defined in clean by default (i.e., inputs=model.inputs)
bounds (optional)	$n \times 2$ ndarray	upper and lower bounds for 95% confidence interval of predicting; returned if ReturnBounds=True

coverage3

mean, bounds, rmse = model.coverage3(**kwargs)

For validation testing of a FoKL model. Default functionality is to evaluate all inputs (i.e., train and test sets) using evaluate. Returned is the predicted output mean, 95% confidence bounds bounds, and Root Mean Square Error rmse. A plot may be returned by setting plot=True; or, for a potentially more meaningful plot in terms of judging accuracy, plot='sorted' will plot the data in increasing value.

To govern what is passed to evaluate:

Keyword	Type	Description	Default
inputs	-	see model.inputs of clean	model.inputs
data	-	see model.data of clean	model.data
draws	-	see draws of FoKL	model.draws

To govern basic plot controls:

Keyword	Type	Description	Default
plot	boolean or string	for generating plot; set to 'sorted' for plot of ordered data	False
bounds	boolean	for plotting bounds	True
xaxis	integer	index of the input variable to plot along the x-axis	indices
labels	boolean	for adding labels to plot	True
xlabel	string	x-axis label	'Index'
ylabel	string	y-axis label	'Data'
title	string	plot title	'FoKL'
legend	boolean	for adding legend to plot	True
LegendLabelFoKL	string	FoKL's label in legend	'FoKL'
LegendLabelData	string	Data's label in legend	'Data'
LegendLabelBounds	string	Bounds's label in legend	'Bounds'

To govern detailed plot controls:

Keyword	Type	Description	Default
PlotTypeFoKL	string	FoKL's color and line type	'b'
PlotSizeFoKL	scalar	FoKL's line size	2
PlotTypeBounds	string	Bounds' color and line type	'k--'
PlotSizeBounds	scalar	Bounds' line size	2
PlotTypeData	string	Data's color and line type	'ro'
PlotSizeData	scalar	Data's line size	2

Output	Type	Description
mean	-	see mean of evaluate
bounds	-	see bounds of evaluate
rmse	scalar	Root Mean Squared Error (RMSE) of prediction in relation to data

fit

betas, mtx, evs = model.fit(inputs=None, data=None, **kwargs)

Training routine for fitting model to known inputs and data.

Input	Type	Description	Default
inputs	-	see traininputs of trainset	traininputs, _ = model.trainset()
data	-	see traindata of trainset	_, traindata = model.trainset()

Keyword	Type	Description	Default
clean	boolean	pass inputs and data to clean if true	False
ConsoleOutput	boolean	print [ind, ev] to console during FoKL model generation; will print percent completed of each Gibbs sampler call prior to [ind, ev] if large dataset (i.e., if less than 64-bit was requested in clean)	True

If clean=True, then any keywords documented for clean may be used here.

Output	Type	Description
betas	$draws \times terms$ ndarray	draws from the posterior distribution of coefficients, with rows corresponding to draws (i.e., a single set of coefficients) and columns corresponding to terms in the model (i.e., $\beta_0, \beta_1, \dots $)
mtx	$(terms-1) \times m$ ndarray	interaction matrix defining order of basis function for term/variable combinations in FoKL model, with rows corresponding to terms (i.e., columns of betas beyond the first column) and columns corresponding to input variables (i.e., columns of model.inputs)
evs	ndarray	vector of BIC values corresponding to each proposed model during training

clear

model.clear(keep=None, clear=None, all=False)

Delete all attributes from the FoKL class except for hyperparameters and settings, unless otherwise specified by the clear keyword. If an attribute is listed in both the clear and keep keywords, then the attribute is cleared.

Input	Type	Description	Default
keep	list of strings	attributes to keep in addition to hyperparameters and settings, e.g., keep=['inputs', 'mtx']	model.keep
clear	list of strings	hyperparameters to delete, e.g., clear=['kernel', 'phis']	None
all	boolean	if True then all attributes (including hyperparameters) get deleted regardless	False

Note when the FoKL class was initialized, model.keep got defined by default as a list of strings including the names of all hyperparameters and settings. These then get preserved here by default.

To remove all attributes from the class, simply call:

model.clear(all=True)

to_pyomo

m = model.to_pyomo(xvars, yvars, m=None, xfix=None, yfix=None, truescale=True, std=True, draws=None)

Pass arguments to fokl_to_pyomo. If embedding a single GP in Pyomo rather than multiple, it is recommended to use this method to avoid importing an additional module in the run script.

save

filepath = model.save(filename=None, directory=None)

Save a FoKL class as a file with extension '.fokl'. If not saving where expected relative to the run script, ensure the terminal directory is set to that of the run script.

Both inputs are optional. By default, filename is of the form 'model_yyyymmddhhmmss.fokl' and is saved to the current directory. To change the directory, embed within filename or assign to directory if using the default filename format.

Returned is filepath. Enter this as the argument to load to later reload the model. Explicitly, that is:

FoKLRoutines.load(filepath)

Input	Type	Description
filename	string	name of file to save model as (note '.fokl' extension can be automatically or manually appended)
directory	string	absolute or relative path to pre-existing folder in which to save filename

Output	Type	Description
filepath	string	absolute path to where the file was saved

fokl_to_pyomo

from FoKL.fokl_to_pyomo import fokl_to_pyomo
m = fokl_to_pyomo(models, xvars, yvars, m=None, xfix=None, yfix=None, truescale=True, std=True, draws=None)

Embed GP's in Pyomo by automatically converting draws from FoKL models trained with or defined by the 'Bernoulli Polynomials' kernel to symbolic expressions in a Pyomo model.

Defining the Pyomo model's objective and any other constraints must be done outside of fokl_to_pyomo. The user must then define an appropriate solver for the Pyomo model. The following is known to work for global optimization problems, which will likely be required for a problem using a GP model.

solver = pyo.SolverFactory('multistart')
solver.solve(m, solver='ipopt')

For documentation on the components of the Pyomo model automatically generated, see nomenclature_of_fokl_to_pyomo.ipynb. The function arguments are as follows.

Input	Type	Description	Default
models	list of FoKL class objects	multiple FoKL models to be embedded in single Pyomo model	-
xvars	list of lists of strings	strings are input variable names, and lists correspond to models	-
yvars	list of strings	strings are output variable names corresponding to models	-
m	Pyomo model	pre-existing Pyomo model if existing	pyo.ConcreteModel()
xfix	list of lists of floats	floats are input variable values if known and to be fixed), and lists correspond to models	-
yfix	list of floats	floats are output variable values (if known and to be fixed) corresponding to models	-
truescale	list of lists of booleans	corresponding to the variables created by xvars, set True to use true scale (i.e., un-normalized) values and set False to use normalized values; unless xvars is to correspond to the normalized input variables, leave blank	[[True, ..., True], ..., [True, ..., True]]
std	list of booleans	set False if standard deviation of FoKL model (corresponding to position in list) is not needed so Pyomo model only defines mean	[[True, ..., True], ..., [True, ..., True]]
draws	int	number of most recent draws to embed in Pyomo	model.draws

getKernels

For internal use.

This package is used by FoKL during initialization to return the data structure phis, containing coefficients for the basis functions specified by kernel.

import FoKL.getKernels
phis = getKernels.sp500()  # kernel == 'Cubic Splines'
phis = getKernels.bernoulli()  # kernel == 'Bernoulli Polynomials'

GP_integrate

from FoKL.GP_Integrate import GP_Integrate
T, Y = GP_Integrate(betas, matrix, b, norms, phis, start, stop, y0, h, used_inputs)

Integrate FoKL models that were fitted to derivatives. Multiple models are able to be integrated simulatneously. Currently, only models trained on the "Cubic Splines" basis functions are supported.

For example, training model1 on $x = f(\dot{x}, b_1)$ and model2 on $y = f(\dot{y}, b_2)$ is as usual. Then, to integrate the models with constants $(b_1, b_2)$ set to b and initial conditions $(x_0, y_0)$ set to y0,

betas1, mtx1, _ = model1.fit([xdot, b1], x)
betas2, mtx2, _ = model2.fit([ydot, b2], y)

T, Y = GP_integrate([np.mean(betas1, axis=0), np.mean(betas2, axis=0)], 
                    [mtx1, mtx2], 
                    [b1, b2], 
                    ..., 
                    [x0, y0], 
                    ...)

Input	Description
betas	betas is a list of arrays in which each entry to the list contains a specific row of the betas matrix, or the mean of the betas matrix for each model being integrated.
matrix	matrix is a list of arrays containing the interaction matrix of each model.
b	b is an array of the values of all the other inputs to the model(s) (including any forcing functions) over the time period we integrate over. The length of b should be equal to the number of points in the final time series (stop - start) / h. All values in b need to be normalized with respect to the min and max values of their respective values in the training dataset.
norms	norms is a matrix of the min and max values of all the inputs being integrated (in the same order as y0). Min values are in the top row, max values in the bottom.
phis	phis is a data structure with coefficients for basis functions.
start	start is the time at which integration begins.
stop	stop is the time to end integration.
y0	y0 is an array of the inital conditions for the models being integrated.
h	h is the step size with respect to time.
used_inputs	used_inputs is a list of arrays containing the information as to what inputs are used in what model. Each array should contain a vector corresponding to a different model. Inputs should be referred to as those being integrated first, followed by those contained in b (in the same order as they appear in y0 and b respectively).

Output	Description
T	T is an array of the time steps the models are integrated at.
Y	Y is an array of the models that have been integrated, at the time steps contained in T.

To demonstrate used_inputs, suppose two models were being integrated with 3 other inputs total. The 1st model uses the output of both models as inputs; and, the 1st and 3rd additional inputs. The 2nd model uses its own output as an input; and, the 2nd and 3rd additional inputs. This yields

used_inputs = [[1, 1, 1, 0, 1], [0, 1, 0, 1, 0]]

If the models created do not follow this ordering scheme for their inputs, the inputs can be rearranged based upon an alternate numbering scheme provided to used_inputs. E.g., if the inputs need to be reordered then the 1st input should have a '1' in its place in the used_inputs vector, the 2nd input should have a '2', and so on. Using the same example as before, if the 1st model's inputs need to be rearranged so that the 3rd additional input comes first, followed by the two model outputs in the same order as they are in y0, and ends with the 1st additional input, then the 1st list in used_inputs would be [2, 3, 4, 0, 1].

Benchmarks and Papers

As mentioned in About FoKL, the primary advantage offered by FoKL in comparison to other machine learning packages is a significant decrease in computation time for training a model while not experiencing a significant decrease in accuracy. This holds true for most datasets but especially for those with an underlying static or dynamic relationship as is often the case in any physical science experiment.

The following paper outlines the methodology of FoKL and includes two example problems.

• Fast variable selection makes Karhunen-Loève decomposed Gaussian process BSS-ANOVA a speedy and accurate choice for dynamic systems identification

The two example problems are:

• ‘Susceptible, Infected, Recovered’ (SIR) toy problem
• ‘Cascaded Tanks’ experimental dataset for a benchmark

Future Development

FoKL-GPy is actively in development. Current focus is on:

• Pyomo
• optimization of code and integration with faster C++ routines
• adding examples for better comparisons and benchmarks
• more robust tutorials

Please reach out via the information in the Contact Us section with any suggestions for development.

Contact Us

Topic	Point of Contact	Email
Installation Troubleshooting Development	Jacob Krell	[email protected]
Research Theory Other	David Mebane	[email protected]

License

FoKL-GPy has an MIT license. Please see the LICENSE file.

Citations

Please cite: K. Hayes, M.W. Fouts, A. Baheri and D.S. Mebane, "Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Loève decomposed Gaussian processes", arXiv:2205.13676

Credits: David Mebane (ideas and original code), Kyle Hayes (integrator), Derek Slack (Python porting), Jacob Krell (Python v3 dev.)

Funding provided by National Science Foundation, Award No. 2119688